| 1. |
- Adjiashvili, David, et al.
(författare)
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Exact and Approximation Algorithms for Optimal Equipment Selection in Deploying In-Building Distributed Antenna Systems
- 2015
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Ingår i: IEEE Transactions on Mobile Computing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1536-1233 .- 1558-0660. ; 14:4, s. 702-713
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Tidskriftsartikel (refereegranskat)abstract
- We consider a combinatorial optimization problemin passive In-Building Distributed Antenna Systems (IB-DAS) deployment for indoor mobile broadband service. These systems have a tree topology, in which a central base station is connected to a number of antennas located at tree leaves via cables represented by the tree edges. Each inner node corresponds to a power equipment, of which the available types differ in the number of output ports and/or by power gain at the ports. This paper focuses on the equipment selection problemthat amounts to, for a given passive DAS tree topology, selecting a power equipment type for each inner node and assigning the outgoing edges of the node to the equipment ports. The performance metric is the power deviation at the antennas from the target values. We consider as objective function the minimization of either the total or the largest power deviation over all antennas. Our contributions are the development of exact pseudo-polynomial time algorithms and (additive) fully-polynomial time approximation schemes for both objectives. Numerical results are provided to illustrate the algorithms. We also extend some results to account for equipment cost.
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| 2. |
- Ahani, Ghafour, et al.
(författare)
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Optimal Content Caching and Recommendation With Age of Information
- 2024
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Ingår i: IEEE Transactions on Mobile Computing. - : IEEE Computer Society. - 1536-1233 .- 1558-0660. ; 23:1, s. 689-704
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Tidskriftsartikel (refereegranskat)abstract
- Content caching at the network edge is an effective way of mitigating backhaul load and improving user experience. Caching efficiency can be enhanced by content recommendation and by keeping the information fresh. By content recommendation, a requested content that is not in the cache can be alternatively satisfied by a related cached content recommended by the system. Information freshness can be quantified by age of information (AoI). This article has the following contributions. First, we address optimal scheduling of cache updates for a time-slotted system accounting for content recommendation and AoI, and to the best of our knowledge, there is no work that has jointly taken into account these aspects. Next, we rigorously prove the problem's NP-hardness. Then, we derive an integer linear formulation, by which the optimal solution can be obtained for small-scale scenarios. On the algorithmic side, our contributions include the development of an effective algorithm based on Lagrangian decomposition, and efficient algorithms for solving the resulting subproblems. Our algorithm computes a bound that can be used to evaluate the performance of any suboptimal solution. We conduct simulations to show the effectiveness of our algorithm.
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| 3. |
- Ahani, Ghafour, et al.
(författare)
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Optimal Scheduling of Age-centric Caching : Tractability and Computation
- 2022
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Ingår i: IEEE Transactions on Mobile Computing. - : IEEE. - 1536-1233 .- 1558-0660 .- 2161-9875. ; 21, s. 2939-2954
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Tidskriftsartikel (refereegranskat)abstract
- The notion of age of information (AoI) has become an important performance metric in network and control systems. Information freshness, represented by AoI, naturally arises in the context of caching. We address optimal scheduling of cache updates for a time-slotted system where the contents vary in size. There is limited capacity for the cache for making updates. Each content is associated with a utility function that depends on the AoI and the time duration of absence from the cache. For this combinatorial optimization problem, we present the following contributions. First, we provide theoretical results of problem tractability. Whereas the problem is NP-hard, we prove solution tractability in polynomial time for a special case with uniform content size, by a reformulation using network flows. Second, we derive an integer linear formulation for the problem, of which the optimal solution can be obtained for small-scale scenarios. Next, via a mathematical reformulation, we derive a scalable optimization algorithm using repeated column generation. In addition, the algorithm computes a bound of global optimum, that can be used to assess the performance of any scheduling solution. Performance evaluation of large-scale scenarios demonstrates the strengths of the algorithm in comparison to a greedy schedule. Finally, we extend the applicability of our work to cyclic scheduling.
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| 4. |
- Alpcan, Tansu, et al.
(författare)
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Security Games for Vehicular Networks
- 2011
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Ingår i: IEEE Transactions on Mobile Computing. - 1536-1233 .- 1558-0660. ; 10:2, s. 280-290
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Tidskriftsartikel (refereegranskat)abstract
- Vehicular networks (VANETs) can be used to improve transportation security, reliability, and management. This paper investigates security aspects of VANETs within a game-theoretic framework where defensive measures are optimized with respect to threats posed by malicious attackers. The formulations are chosen to be abstract on purpose in order to maximize applicability of the models and solutions to future systems. The security games proposed for vehicular networks take as an input centrality measures computed by mapping the centrality values of the car networks to the underlying road topology. The resulting strategies help locating most valuable or vulnerable points (e.g., against jamming) in vehicular networks. Thus, optimal deployment of traffic control and security infrastructure is investigated both in the static (e.g., fixed roadside units) and dynamic cases (e. g., mobile law enforcement units). Multiple types of security games are studied under varying information availability assumptions for the players, leading to fuzzy game and fictitious play formulations in addition to classical zero-sum games. The effectiveness of the security game solutions is evaluated numerically using realistic simulation data obtained from traffic engineering systems.
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| 5. |
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| 6. |
- Baghersalimi, Saleh, et al.
(författare)
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Decentralized Federated Learning for Epileptic Seizures Detection in Low-Power Wearable Systems
- 2023
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Ingår i: IEEE Transactions on Mobile Computing. - 1536-1233. ; , s. 1-16
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Tidskriftsartikel (refereegranskat)abstract
- In healthcare, data privacy of patients regulations prohibits data from being moved outside the hospital, preventing international medical datasets from being centralized for AI training. Federated learning (FL) is a data privacy-focused method that trains a global model by aggregating local models from hospitals. Existing FL techniques adopt a central server-based network topology, where the server assembles the local models trained in each hospital to create a global model. However, the server could be a point of failure, and models trained in FL usually have worse performance than those trained in the centralized learning manner when the patient's data are not independent and identically distributed (Non-IID) in the hospitals. This paper presents a decentralized FL framework, including training with adaptive ensemble learning and a deployment phase using knowledge distillation. The adaptive ensemble learning step in the training phase leads to the acquisition of a specific model for each hospital that is the optimal combination of local models and models from other available hospitals. This step solves the non-IID challenges in each hospital. The deployment phase adjusts the model's complexity to meet the resource constraints of wearable systems. We evaluated the performance of our approach on edge computing platforms using EPILEPSIAE and TUSZ databases, which are public epilepsy datasets.
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| 7. |
- Biasson, A, et al.
(författare)
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A decentralized optimization framework for energy harvesting devices
- 2018
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Ingår i: IEEE Transactions on Mobile Computing. - 1536-1233 .- 1558-0660. ; 17:11, s. 2483-2496
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Tidskriftsartikel (refereegranskat)abstract
- Designing decentralized policies for wireless communication networks is a crucial problem, which has only been partially solved in the literature so far. In this paper, we propose a Decentralized Markov Decision Process (Dec-MDP) framework to analyze a wireless sensor network with multiple users which access a common wireless channel. We consider devices with energy harvesting capabilities, that aim at balancing the energy arrivals with the data departures and with the probability of colliding with other nodes. Over time, an access point triggers a SYNC slot, wherein it recomputes the optimal transmission parameters of the whole network, and distributes this information. Every node receives its own policy, which specifies how it should access the channel in the future, and, thereafter, proceeds in a fully decentralized fashion, with no interactions with other entities in the network. We propose a multi-layer Markov model, where an external MDP manages the jumps between SYNC slots, and an internal Dec-MDP computes the optimal policy in the short term. We numerically show that, because of the harvesting, stationary policies are suboptimal in energy harvesting scenarios, and the optimal trade-off lies between an orthogonal and a random access system.
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| 8. |
- Chaudhary, Rajat, et al.
(författare)
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SecGreen : Secrecy Ensured Power Optimization Scheme for Software-Defined Connected IoV
- 2023
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Ingår i: IEEE Transactions on Mobile Computing. - : IEEE Computer Society. - 1536-1233 .- 1558-0660. ; 22:4, s. 2370-2386
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Tidskriftsartikel (refereegranskat)abstract
- Software-Defined Internet of Vehicles (SD-IoV) is an emerging technology that is being used in modern intelligent transportation systems (ITS). The ultimate goal of SD-IoV is to provide seamless connectivity to the end-users with low latency and high-speed data transfer. However, due to the increase in the density of the connected IoV using an open channel, i.e., the Internet, the foremost challenges of high power consumption and secure data transfer are inevitable in such an environment. An external eavesdropper may intercept the transmitted message to access the legitimate information over the public channel, i.e., the Internet. Most of the solutions reported in the literature to tackle these issues may not be applicable in the SD-IoV environment due to high computation and communication costs. Motivated from this, in this paper, the problems of high power consumption and secure data transfer in SD-IoV are formulated using mixed-integer non-linear programming (MINLP) with associated constraints. To solve the aforementioned problem, we propose a joint power optimization and secrecy ensured scheme known as SecGreen. SecGreen has an efficient energy harvesting algorithm using simultaneous wireless information and power transfer (SWIPT) to maximize the energy efficiency. Moreover, to mitigate various security attacks, a resilient lightweight secrecy association protocol is designed between vehicle and trusted gateway node of SD-IoV so that only trusted vehicles can communicate with each other and with the nearest base stations. The secrecy association protocol uses security primitives such as- physically unclonable function (PUF), one-way hash function, and bitwise exclusive OR (XOR) operations which are suitable for energy-constraint sensors in SD-IoV. The performance of the SecGreen is compared with the existing schemes, Stable & Scalable Link Optimization (SSLO), and Secure & Energy-Efficient Blockchain-enabled (SEEB) respectively. The result shows that when the number of packets across the subchannel increases, the energy consumption increases. Also, the result shows that the proposed scheme attains 22.5% and 20.34% better energy efficiency as compared to SSLO and SEEB schemes, respectively. In addition, the SecGreen scheme achieves 37.48% and 32.15% higher throughput as compared to SSLO and SEEB schemes. The results obtained show the superior performance of the proposed SecGreen scheme in comparison to these existing competitive schemes in the literature.
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| 9. |
- Combes, Richard, et al.
(författare)
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Optimal Rate Sampling in 802.11 Systems : Theory, Design, and Implementation
- 2019
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Ingår i: IEEE Transactions on Mobile Computing. - : IEEE COMPUTER SOC. - 1536-1233 .- 1558-0660. ; 18:5, s. 1145-1158
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Tidskriftsartikel (refereegranskat)abstract
- Rate Adaptation (RA) is a fundamental mechanism in 802.11 systems. It allows transmitters to adapt the coding and modulation scheme as well as the MIMO transmission mode to the radio channel conditions, to learn and track the (mode, rate) pair providing the highest throughput. The design of RA mechanisms has been mainly driven by heuristics. In contrast, we rigorously formulate RA as an online stochastic optimization problem. We solve this problem and present G-ORS (Graphical Optimal Rate Sampling), a family of provably optimal (mode, rate) pair adaptation algorithms. Our main result is that G-ORS outperforms state-of-the-art algorithms such as MiRA and Minstrel HT as demonstrated by experiments on a 802.11n network test-bed. The design of G-ORS is supported by a theoretical analysis, where we study its performance in stationary radio environments where the successful packet transmission probabilities at the various (mode, rate) pairs do not vary over time, and in non-stationary environments where these probabilities evolve. We show that under G-ORS, the throughput loss due to the need to explore sub-optimal (mode, rate) pairs does not depend on the number of available pairs. This is a crucial advantage as evolving 802.11 standards offer an increasingly large number of (mode, rate) pairs. We illustrate the superiority of G-ORS over state-of-the-art algorithms, using both trace-driven simulations and test-bed experiments.
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| 10. |
- Curescu, C., et al.
(författare)
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A bidding algorithm for optimized utility-based resource allocation in ad hoc networks
- 2008
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Ingår i: IEEE Transactions on Mobile Computing. - 1536-1233 .- 1558-0660. ; 7:12, s. 1397-1414
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a scheme for bandwidth allocation in wireless ad hoc networks. The quality-of-service (QoS) levels for each end-to-end flow are expressed using a resource-utility function, and our algorithms aim to maximize aggregated utility. The shared channel is modeled as a bandwidth resource defined by maximal cliques of mutual interfering links. We propose a novel resource allocation algorithm that employs an auction mechanism in which flows are bidding for resources. The bids depend both on the flow's utility function and the intrinsically derived shadow prices. We then combine the admission control scheme with a utility-aware on-demand shortest path routing algorithm where shadow prices are used as a natural distance metric. As a baseline for evaluation, we show that the problem can be formulated as a linear programming (LP) problem. Thus, we can compare the performance of our distributed scheme to the centralized LP solution, registering results very close to the optimum. Next, we isolate the performance of price-based routing and show its advantages in hotspot scenarios, and also propose an asynchronous version that is more feasible for ad hoc environments. Further experimental evaluation compares our scheme with the state of the art derived from Kelly's utility maximization framework and shows that our approach exhibits superior performance for networks with increased mobility or less frequent allocations. © 2008 IEEE.
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